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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                        第10节：NAN和INF值处理.md
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                        第11节：random模块.md
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                        第12节：axis理解.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
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                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
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                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                        第2节：echarts和pyecharts库
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                        第3节：bokeh库
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                        <h1 id="pandas&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97;">Pandas&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97; </h1>
<p>&#x662F;&#x6570;&#x636E;&#x6E05;&#x6D17;&#x7684;&#x91CD;&#x8981;&#x8FC7;&#x7A0B;&#xFF0C;&#x53EF;&#x4EE5;&#x6309;&#x7D22;&#x5F15;&#x5BF9;&#x9F50;&#x8FDB;&#x884C;&#x8FD0;&#x7B97;&#xFF0C;&#x5982;&#x679C;&#x6CA1;&#x5BF9;&#x9F50;&#x7684;&#x4F4D;&#x7F6E;&#x5219;&#x8865;NaN&#xFF0C;&#x6700;&#x540E;&#x4E5F;&#x53EF;&#x4EE5;&#x586B;&#x5145;NaN</p>
<blockquote>
<h2 id="series&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97;">Series&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97; </h2>
</blockquote>
<h4 id="1-series-&#x6309;&#x884C;&#x3001;&#x7D22;&#x5F15;&#x5BF9;&#x9F50;">1. Series &#x6309;&#x884C;&#x3001;&#x7D22;&#x5F15;&#x5BF9;&#x9F50; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python">s1 = pd.Series(range(<span class="hljs-number">10</span>, <span class="hljs-number">20</span>), index = range(<span class="hljs-number">10</span>))
s2 = pd.Series(range(<span class="hljs-number">20</span>, <span class="hljs-number">25</span>), index = range(<span class="hljs-number">5</span>))

print(<span class="hljs-string">&apos;s1: &apos;</span> )
print(s1)

print(<span class="hljs-string">&apos;&apos;</span>) 

print(<span class="hljs-string">&apos;s2: &apos;</span>)
print(s2)
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>s1: 
0    10
1    11
2    12
3    13
4    14
5    15
6    16
7    17
8    18
9    19
dtype: int64

s2: 
0    20
1    21
2    22
3    23
4    24
dtype: int64
</code></pre><h4 id="2-series&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97;">2. Series&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># Series &#x5BF9;&#x9F50;&#x8FD0;&#x7B97;</span>
s1 + s2
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>0    30.0
1    32.0
2    34.0
3    36.0
4    38.0
5     NaN
6     NaN
7     NaN
8     NaN
9     NaN
dtype: float64
</code></pre><blockquote>
<h2 id="dataframe&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97;">DataFrame&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97; </h2>
</blockquote>
<h4 id="1-dataframe&#x6309;&#x884C;&#x3001;&#x5217;&#x7D22;&#x5F15;&#x5BF9;&#x9F50;">1. DataFrame&#x6309;&#x884C;&#x3001;&#x5217;&#x7D22;&#x5F15;&#x5BF9;&#x9F50; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python">df1 = pd.DataFrame(np.ones((<span class="hljs-number">2</span>,<span class="hljs-number">2</span>)), columns = [<span class="hljs-string">&apos;a&apos;</span>, <span class="hljs-string">&apos;b&apos;</span>])
df2 = pd.DataFrame(np.ones((<span class="hljs-number">3</span>,<span class="hljs-number">3</span>)), columns = [<span class="hljs-string">&apos;a&apos;</span>, <span class="hljs-string">&apos;b&apos;</span>, <span class="hljs-string">&apos;c&apos;</span>])

print(<span class="hljs-string">&apos;df1: &apos;</span>)
print(df1)

print(<span class="hljs-string">&apos;&apos;</span>) 
print(<span class="hljs-string">&apos;df2: &apos;</span>)
print(df2)
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code>df1: 
     a    b
0  1.0  1.0
1  1.0  1.0

df2: 
     a    b    c
0  1.0  1.0  1.0
1  1.0  1.0  1.0
2  1.0  1.0  1.0
</code></pre><h4 id="2-dataframe&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97;">2. DataFrame&#x7684;&#x5BF9;&#x9F50;&#x8FD0;&#x7B97; </h4>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># DataFrame&#x5BF9;&#x9F50;&#x64CD;&#x4F5C;</span>
df1 + df2
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code class="lang-python">     a    b   c
<span class="hljs-number">0</span>  <span class="hljs-number">2.0</span>  <span class="hljs-number">2.0</span> NaN
<span class="hljs-number">1</span>  <span class="hljs-number">2.0</span>  <span class="hljs-number">2.0</span> NaN
<span class="hljs-number">2</span>  NaN  NaN NaN
</code></pre>
<blockquote>
<h2 id="&#x586B;&#x5145;&#x672A;&#x5BF9;&#x9F50;&#x7684;&#x6570;&#x636E;&#x8FDB;&#x884C;&#x8FD0;&#x7B97;">&#x586B;&#x5145;&#x672A;&#x5BF9;&#x9F50;&#x7684;&#x6570;&#x636E;&#x8FDB;&#x884C;&#x8FD0;&#x7B97; </h2>
</blockquote>
<h4 id="1-fillvalue">1. fill_value </h4>
<blockquote>
<p>&#x4F7F;&#x7528;<code>add</code>,<code>sub</code>,<code>div</code>,<code>mul</code>&#x7684;&#x540C;&#x65F6;&#xFF0C;</p>
<p>&#x901A;&#x8FC7;<code>fill_value</code>&#x6307;&#x5B9A;&#x586B;&#x5145;&#x503C;&#xFF0C;&#x672A;&#x5BF9;&#x9F50;&#x7684;&#x6570;&#x636E;&#x5C06;&#x548C;&#x586B;&#x5145;&#x503C;&#x505A;&#x8FD0;&#x7B97;</p>
</blockquote>
<p>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#xFF1A;</p>
<pre><code class="lang-python">print(s1)
print(s2)
s1.add(s2, fill_value = -<span class="hljs-number">1</span>)

print(df1)
print(df2)
df1.sub(df2, fill_value = <span class="hljs-number">2.</span>)
</code></pre>
<p>&#x8FD0;&#x884C;&#x7ED3;&#x679C;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-comment"># print(s1)</span>
<span class="hljs-number">0</span>    <span class="hljs-number">10</span>
<span class="hljs-number">1</span>    <span class="hljs-number">11</span>
<span class="hljs-number">2</span>    <span class="hljs-number">12</span>
<span class="hljs-number">3</span>    <span class="hljs-number">13</span>
<span class="hljs-number">4</span>    <span class="hljs-number">14</span>
<span class="hljs-number">5</span>    <span class="hljs-number">15</span>
<span class="hljs-number">6</span>    <span class="hljs-number">16</span>
<span class="hljs-number">7</span>    <span class="hljs-number">17</span>
<span class="hljs-number">8</span>    <span class="hljs-number">18</span>
<span class="hljs-number">9</span>    <span class="hljs-number">19</span>
dtype: int64

<span class="hljs-comment"># print(s2)</span>
<span class="hljs-number">0</span>    <span class="hljs-number">20</span>
<span class="hljs-number">1</span>    <span class="hljs-number">21</span>
<span class="hljs-number">2</span>    <span class="hljs-number">22</span>
<span class="hljs-number">3</span>    <span class="hljs-number">23</span>
<span class="hljs-number">4</span>    <span class="hljs-number">24</span>
dtype: int64

<span class="hljs-comment"># s1.add(s2, fill_value = -1)</span>
<span class="hljs-number">0</span>    <span class="hljs-number">30.0</span>
<span class="hljs-number">1</span>    <span class="hljs-number">32.0</span>
<span class="hljs-number">2</span>    <span class="hljs-number">34.0</span>
<span class="hljs-number">3</span>    <span class="hljs-number">36.0</span>
<span class="hljs-number">4</span>    <span class="hljs-number">38.0</span>
<span class="hljs-number">5</span>    <span class="hljs-number">14.0</span>
<span class="hljs-number">6</span>    <span class="hljs-number">15.0</span>
<span class="hljs-number">7</span>    <span class="hljs-number">16.0</span>
<span class="hljs-number">8</span>    <span class="hljs-number">17.0</span>
<span class="hljs-number">9</span>    <span class="hljs-number">18.0</span>
dtype: float64


<span class="hljs-comment"># print(df1)</span>
     a    b
<span class="hljs-number">0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>
<span class="hljs-number">1</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>

<span class="hljs-comment"># print(df2)</span>
     a    b    c
<span class="hljs-number">0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>
<span class="hljs-number">1</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>
<span class="hljs-number">2</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>


<span class="hljs-comment"># df1.sub(df2, fill_value = 2.)</span>
     a    b    c
<span class="hljs-number">0</span>  <span class="hljs-number">0.0</span>  <span class="hljs-number">0.0</span>  <span class="hljs-number">1.0</span>
<span class="hljs-number">1</span>  <span class="hljs-number">0.0</span>  <span class="hljs-number">0.0</span>  <span class="hljs-number">1.0</span>
<span class="hljs-number">2</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>  <span class="hljs-number">1.0</span>
</code></pre>
<p><strong>&#x7B97;&#x672F;&#x65B9;&#x6CD5;&#x8868;</strong>:</p>
<table>
<thead>
<tr>
<th style="text-align:left">&#x65B9;&#x6CD5;</th>
<th style="text-align:left">&#x63CF;&#x8FF0;</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">add&#xFF0C;radd</td>
<td style="text-align:left">&#x52A0;&#x6CD5;&#xFF08;+&#xFF09;</td>
</tr>
<tr>
<td style="text-align:left">sub&#xFF0C;rsub</td>
<td style="text-align:left">&#x51CF;&#x6CD5;&#xFF08;-&#xFF09;</td>
</tr>
<tr>
<td style="text-align:left">div&#xFF0C;rdiv</td>
<td style="text-align:left">&#x9664;&#x6CD5;&#xFF08;/&#xFF09;</td>
</tr>
<tr>
<td style="text-align:left">floordiv&#xFF0C;rfllordiv</td>
<td style="text-align:left">&#x6574;&#x9664;&#xFF08;//&#xFF09;</td>
</tr>
<tr>
<td style="text-align:left">mul&#xFF0C;rmul</td>
<td style="text-align:left">&#x4E58;&#x6CD5;&#xFF08;*&#xFF09;</td>
</tr>
<tr>
<td style="text-align:left">pow&#xFF0C;rpow</td>
<td style="text-align:left">&#x5E42;&#x6B21;&#x65B9;&#xFF08;**&#xFF09;</td>
</tr>
</tbody>
</table>

                    
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